Cognitive neuroimaging methods are well regarded as powerful research tools for studying the neural correlates of both health and disease, and have led to the generation of enormous amounts of data. As a result, quantitative meta-analysis methods have been developed and adopted by the community as a means to organize and synthesize large-scale data sets. Meta-analyses allow diverse results regarding cognitive and cortical dysfunction in disease and treatment studies to be assessed, and the most reliable patterns of results to be determined. However, the most labor-intensive step of these procedures is identification of the appropriate literature. Currently, researchers manually execute multiple PubMed searches utilizing different keywords from alternate terminologies to capture the entirety of the studies they seek. The Cognitive Paradigm Ontology (CogPO) was created in 2009 to address the non-standard vocabulary that exists for describing behavioral tasks or paradigms in brain mapping experiments. Here, we propose to leverage the National Center for Biomedical Ontologies' bioinformatics tools to integrate CogPO and the NCBO Annotator, to develop and test a new computational resource, BrainMap Tracker, that will enable automatic identification of candidate studies for neuroimaging meta-analyses. This tool will allow rapid filtering of PubMed abstracts to identify what paradigms have been utilized to study brain activations for a given disease, or vice versa. The proposed system will be a novel web-based resource for cognitive and clinical neuroscientists to provide coherent groupings of studies suitable for meta-analysis. BrainMap Tracker will alleviate the problem of incomplete neuroimaging meta-analyses, provide additional informatics support through semantic annotations for large scale text-mining and data visualization efforts, and offer initial recommendations for new annotation terms for better categorization of new studies.